Open AccessDesign of fully interpretable neural networks for digital coherent demodulation
Author(s) -
Xiatao Huang,
Wenshan Jiang,
Xingwen Yi,
Jing Zhang,
Taowei Jin,
Qianwu Zhang,
Bo Xu,
Kun Qiu
Publication year - 2022
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.472406
Subject(s) - demodulation , digital signal processing , computer science , artificial neural network , quadrature amplitude modulation , multi mode optical fiber , electronic engineering , nonlinear system , multiplexing , compensation (psychology) , apodization , signal processing , optics , telecommunications , optical fiber , artificial intelligence , bit error rate , physics , computer hardware , engineering , channel (broadcasting) , psychology , quantum mechanics , psychoanalysis
In this paper, we propose a digital coherent demodulation architecture using fully interpretable deep neural networks (NNs). We show that all the conventional coherent digital signal processing (DSP) is deeply unfolded into a well-structured NN so that the established training algorithms in machine learning can be applied. In contrast to adding or replacing certain algorithms of existing DSP in coherent receivers, we replace all the coherent demodulation algorithms with a fully interpretable NN (FINN), making the whole NN interpretable. The FINN is modular and flexible to add or drop modules, including chromatic dispersion compensation (CDC), the digital back-propagation (DBP) algorithm for fiber nonlinearity compensation, carrier recovery and residual impairments. The resulted FINN can be quickly initialized by straightforwardly referring to the conventional DSP, and can also enjoy further performance enhancement in the nonlinear fiber transmissions by NN. We conduct a 132-Gb/s polarization multiplexed (PM)-16QAM transmission experiment over 600-km standard single mode fiber. The experimental results show that without fiber nonlinearity compensation, FINN-CDC obtains less than 0.06-dB SNR gain than chromatic dispersion compensation (CDC). However, with fiber nonlinearity compensation, 2-steps per span FINN-DBP (FINN-2sps-DBP) and FINN-1sps-DBP bring about 0.59-dB and 0.53-dB SNR improvement compared with the conventional 2sps-DBP and 1sps-DBP, respectively.